Missing The Point:

.left-column[

```r
Missing The Point

Non-Convergence 
In Iterative 
Imputation 
Algorithms

Hanne I. Oberman, 
Stef van Buuren, 
& Gerko Vink

ARTEMISS workshop
ICML 2020
```

]

.right-column[ 
# Algorithmic Convergence

With iterative imputation, the validity of inferences relies on algorithmic convergence. Signs of non-convergence (i.e., non-mixing, trending) are typically identified through visual inspection.

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---

.left-column[

```r
Missing The Point

Non-Convergence 
In Iterative 
Imputation 
Algorithms

Hanne I. Oberman, 
Stef van Buuren, 
& Gerko Vink

ARTEMISS workshop
ICML 2020
```

]

.right-column[ 
# Simulation Study

When imputing an incomplete multivariate normal set ( `\(n_{\rm obs}=\)` 1000, `\(n_{\rm sim}=\)` 1000), we obtain valid regression estimates after 5 to 10 iterations.

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]
---

.left-column[

```r
Missing The Point

Non-Convergence 
In Iterative 
Imputation 
Algorithms

Hanne I. Oberman, 
Stef van Buuren, 
& Gerko Vink

ARTEMISS workshop
ICML 2020
```

]

.right-column[ 
# Simulation Study (2)

Whereas non-convergence diagnostics (potential scale reduction factor, auto-correlation) identify signs of non-convergence up-to 30 to 50 iterations.

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]
]
---

.left-column[

```r
Missing The Point

Non-Convergence 
In Iterative 
Imputation 
Algorithms

Hanne I. Oberman, 
Stef van Buuren, 
& Gerko Vink

ARTEMISS workshop
ICML 2020
```

]

.right-column[ 
# Take-away

We conclude that&mdash;in the cases considered&mdash;it never hurts to iterate longer, but such calculations hardly bring added value.

Read more on [github.com/hanneoberman/MissingThePoint](https://github.com/hanneoberman/MissingThePoint). Or follow my updates through Twitter [@hioberman](https://twitter.com/hioberman).

.pull-left[
 
 
 
 
Upcoming...
]

.pull-right[
<img src="https://github.com/gerkovink/shinyMice/blob/edits/ICML/shinymicehex.png?raw=true" width="255">
]
]